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Proceedings Paper

Maximum-likelihood estimation for the two-dimensional discrete Boolean model using cross-windowed observations
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Paper Abstract

The Boolean model is a random set process in which random shapes are positioned according to the outcomes of an independent point process. In the discrete case, the point process is Bernoulli. To do estimation on the two-dimensional discrete Boolean model, we sample the germ-grain model at widely spaced points. An observation using this procedure consists of jointly distributed horizontal and vertical runlengths. An approximate likelihood of each cross observation is computed. Since the observations are taken at widely spaced points, they are considered independent and are multiplied to form a likelihood function for the entire sampled process. Estimation for the two-dimensional process is done by maximizing the grand likelihood over the parameter space. Simulations on random-rectangle Boolean models show significant decrease in variance over the method using horizontal and vertical linear samples. Maximum-likelihood estimation can also be used to fit models to real textures.

Paper Details

Date Published: 25 March 1996
PDF: 11 pages
Proc. SPIE 2662, Nonlinear Image Processing VII, (25 March 1996); doi: 10.1117/12.235819
Show Author Affiliations
John C. Handley, Xerox Corp. (United States)
Edward R. Dougherty, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2662:
Nonlinear Image Processing VII
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham, Editor(s)

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